Georgios Loukas

Protection Against Denial of Service Attacks

Denial of service (DoS) is a prevalent threat in todays networks because DoS attacks are easy to launch, while defending a network resource against them is disproportionately difficult. Despite the extensive research in recent years, DoS attacks continue to harm, as the attackers adapt to the newer protection mechanisms. For this reason, we start our survey with a historical timeline of DoS incidents, where we illustrate the variety of types, targets and motives for such attacks and how they evolved during the last two decades. We then provide an extensive literature review on the existing research on DoS protection with an emphasis on the research of the last years and the most demanding aspects of defence. These include traceback, detection, classification of incoming traffic, response in the presence of an attack and mathematical modelling of attack and defence mechanisms. Our discussion aims to identify the trends in DoS attacks, the weaknesses of protection approaches and the qualities that modern ones should exhibit, so as to suggest new directions that DoS research can follow.

A Denial of Service Detector based on Maximum Likelihood Detection and the Random Neural Network

Due to the simplicity of the concept and the availability of attack tools, launching a DoS attack is relatively easy, while defending a network resource against it is disproportionately difficult. The first step of a protection scheme against DoS must be the detection of its existence, ideally before the destructive traffic build-up. In this paper we propose a DoS detection approach which uses the maximum likelihood criterion with the random neural network (RNN). Our method is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood estimation and fusing the information gathered from the individual input features using likelihood averaging and different architectures of RNNs. We present and compare seven variations of it and evaluate our experimental results obtained in a large networking testbed.

Autonomous networked robots for the establishment of wireless communication in uncertain emergency response scenarios

During a disaster, emergency response operations can benefit from the establishment of a wireless ad hoc network. We propose the use of autonomous robots that move inside a disaster area and establish a network for two-way communication between trapped civilians with uncertain locations and an operation centre. Our aim is to maximise the number of civilians connected to the network. We present a distributed algorithm which involves clustering possible locations of civilians according to their expected shortfall; clustering facilitates both connectivity within groups of civilians and exploration that is based on the uncertainty of these locations. To achieve efficient allocation in terms of time and energy, we also develop a modified algorithm according to which the robots consider the graph that the cluster centres form and follow its minimum spanning tree. We conduct simulations and discuss the efficiency and appropriateness of the two algorithms in different situations.

Robotic wireless network connection of civilians for emergency response operations

Mobile robots equipped with wireless devices can prove very useful during emergency response operations. We envision such robots that locate trapped civilians and initiate an ad hoc network connection between them and the rescuers, so that the latter can better assess the situation and plan the rescue operation accordingly. We present a centralised formulation for the novel problem of optimally allocating robots so that they connect as many civilians as possible, while maintaining their multi-hop connection with a static wireless sink. This formulation stems from a combination of characteristics typically found in assignment and network flow optimisation problems. We have also developed a distributed heuristic with which the robots start from the location of the sink and move autonomously trying to connect the civilians while maintaining connectivity. We evaluate our distributed heuristic using a building evacuation simulator and compare it with the centralised approach.

A Biologically Inspired pired Denial of Service Detector Using the Random Neural Network

Several of todays computing challenges have been met by resorting to and adapting optimal solutions that have evolved in nature. For example, autonomic communication networks have started applying biologically-inspired methods to achieve some of their self-* properties. We build upon such methods to solve the recent problem of detection of denial of service networking attacks, by proposing a combination of Bayesian decision making and the random neural networks (RNN) which are inspired by the random spiking behaviour of the biological neurons. Our approach is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood estimation and fusing the information gathered from the individual input features using different architectures of the RNN. The experiments are conducted using the CPN networking protocol which is also based on the RNN.

Connecting trapped civilians to a wireless ad hoc network of emergency response robots

During a disaster, communications are usually impaired. In order for the rescuers and endangered civilians to communicate, we suggest the use of mobile robots that can act as wireless routers. We describe how they can locate trapped civilians and initiate an ad hoc network connection between them and the rescuers, so that the latter can better assess the situation and plan the rescue operation accordingly. The novel problem that arises is the optimal allocation of these robots so that they connect as many civilians as possible, while maintaining their multi-hop connection with a static wireless sink that represents the group of rescuers. We present a centralised formulation, which stems from a combination of characteristics typically found in assignment and network flow optimisation problems. We consider both exact locations for the civilians and uncertain locations with a probabilistic representation. We also present a distributed heuristic with which the robots start from the location of the sink and move autonomously trying to connect the civilians while maintaining connectivity. We evaluate our distributed heuristics in a building evacuation simulator and compare them with the centralised approach.